In the absence of mutations or exploration, the end states of evolution are inevitably monomorphic: once such a state is reached, it cannot be escaped through imitation. We thus further assume that, with a certain mutation probability, an agent switches randomly to a different strategy without imitating another agent. In the limit of small mutation rates, the dynamics will proceed with, at most, two strategies in the population, such that the behavioral dynamics can be conveniently described by a Markov Chain, where each state represents a monomorphic population, whereas the transition probabilities are given by the fixation probability of a single mutant [1]}, [2]}. The resulting Markov Chain has a stationary distribution, which characterizes the average time the population spends in each of these monomorphic end states.
